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I received my PhD in Computational Mathematics from the Russian Academy of Sciences in 2000 and my MSc in Applied Mathematics from Novosibirsk State University in 1997. I joined Laurier in 2003, initially working as a postdoctoral fellow in the Department of Mathematics for three years. In 2006, I started a tenure-track position as an Assistant Professor. Prior to joining Laurier, I held the position of Assistant Professor of Mathematics at Siberian State University of Telecommunications and Informatics and was a senior research fellow at the Laboratory of Monte Carlo Methods at the Institute of Computational Mathematics and Mathematical Geophysics in Novosibirsk, Russia.
My research has two main foci. The first is the development of financial models for asset pricing and credit risk modelling. These models should provide a good fit for financial data, be tractable, and enable the application of a broad spectrum of analytical and numerical techniques for qualitative analysis. Second, I am interested in developing stochastic, data-driven, and numerical modelling methods for pricing financial securities. Examples include creating efficient stochastic simulation methods for diffusion and jump-diffusion processes, developing spectral expansion techniques for pricing path-dependent derivatives, building statistical machine learning models, and employing parallel computing for pricing financial securities.
Books
Financial Mathematics: A Comprehensive Treatment in Continuous Time (2nd edition published in 2022) (order online from the publisher or Amazon store)
Financial Mathematics: A Comprehensive Treatment in Discrete Time (2nd edition published in 2021) (order online from the publisher or Amazon store)
Financial Mathematics: A Comprehensive Treatment (1st edition) (order online from the publisher or Amazon store)
Research Profiles
- Award in the name of Academician Mikhail A. Lavrentjev for young researchers (Russia, 2001)
- Wilfrid Laurier University Merit Awards (2015, 2017, 2020, 2024)
- Natural Sciences and Engineering Research Council Discovery grants (2007, 2013, 2020)
The research areas I am exploring present numerous problems that are suitable for undergraduate, Master’s, and doctoral students. Between 2006 and 2024, I supervised or co-supervised over 30 Master’s theses and significant research projects. Graduate students from my research program have embarked on successful careers in the financial industry or pursued further studies in financial mathematics. I am open to supervising graduate students in financial mathematics and Monte Carlo methods.
- Jiawei He, J., Makarov, R., Tuero, J., and Wang, Z., Performance evaluation metric for statistical learning trading strategies, Data Science in Finance and Economics, 4(4): 570–600, 2024.
- Campolieti, G., Kato, H., and Makarov, R., Spectral Expansions for Credit Risk Modelling with Occupation Times, Risks, 10(12): 1-20, 2022.
- Campolieti, G. and Makarov R., Solvable Diffusion Models with Linear and Mean-Reverting Nonlinear Drifts, SIAM Journal of Financial Mathematics, 8(1): 146–170, 2017.
- Makarov R., Modelling Liquidation Risk with Occupation Times, International Journal of Financial Engineering, 3(4), 2016.
- Makarov R., Metzler A. and Ni Z., Modelling the Default Risk with Occupation Times, Finance Research Letters, 13, 54-65, 2015.
- Campolieti, G., Makarov, R.N. Financial Mathematics: A Comprehensive Treatment, Chapman & Hall/CRC, 2014.
- Campolieti, G., Makarov, R., and Wouterloot, K. “Pricing step options under the CEV and other solvable diffusion models.” International Journal of Theoretical and Applied Finance, 16(5), 2013.
- Campolieti, G. and Makarov, R.N. “On Properties of Analytically Solvable Families of Local Volatility Diffusion Models.” Mathematical Finance, 22(3):488-518, 2012.
- Campolieti, G. and Makarov, R. “Pricing path-dependent options on state-dependent volatility models with a Bessel bridge.” International Journal of Theoretical and Applied Finance, 10(1):51-88, 2007.
- Makarov, R.N. “Numerical solution of quasilinear parabolic equations and backward stochastic differential equations.” Russ. J. Numer. Anal. Math. Modelling, 18(5):397-412, 2003.
Undergraduate Courses
MA170 Introduction to Mathematics for Finance (2008, 2014, 2015)
MA240 Introduction to Probability and Statistics (2007)
ST259 Probability I (2017, 2018, 2019, 2022, 2023, 2024)
MA270 Financial Mathematics I (2008, 2009, 2014, 2017, 2018, 2020)
MA307 Numerical Analysis (2025)
MA340 Introduction to Probability Theory (2007, 2009)
MA351 Introduction to Stochastic Calculus (2006, 2007, 2010)
MA370 Financial Mathematics II (2009, 2013, 2014, 2024)
MA371 Computational Methods in Mathematics and Statistics (2005, 2007, 2011)
MA371 Computational Methods for Data Analysis (2022, 2024)
MA450 Measure and Integration (2010)
MA470 Financial Mathematics III (2010, 2014, 2016, 2021, 2023)
MA471 Computational Methods in Finance (2016, 2019)
MA485M Quantitative Financial Risk Management (2015)
MA487 Mathematical Modelling in the Applied Sciences and Finance (2017)
MA490/ST490 Stochastic Processes (2013, 2023, 2024, 2025)
MA495H Monte Carlo Methods (2013)
Graduate Courses
MA507 Numerical Analysis (2025)
MA547/MA647 Monte Carlo Simulation Methods (2009, 2011, 2013)
MA570 Financial Mathematics in Discrete Time (2024)
MA571 Computational Methods for Data Analysis (2022, 2024)
MA670 Financial Mathematics: Continuous-Time Option Pricing (2010, 2014, 2016, 2023)
MA680 Seminar in Mathematical Modelling for Science & Finance (2011)
MA685L Quantitative Financial Risk Management (2015)
MA686B/MA671 Computational Finance (2016, 2019)
ST690 Stochastic Processes (2023, 2024, 2025)
MA680 Seminar in Mathematical Modelling in Finance and Science (2023)
MA880 Graduate Seminar in Mathematical and Statistical Modelling (2023)
Contact Info:
Office location: LH3049
Office hours:
As announced on course outlines, in class or on MyLearningSpace. Other times are by appointment.
Languages spoken: English, Russian